A personalized association rule ranking method based on semantic similarity and evolutionary computation

Many methods have been studied for mining association rules efficiently. However, because these methods usually generate a large number of rules, it is still a heavy burden for the users to find the most interesting ones. In this paper, we propose a novel method for finding what the user is interested in by assigning several keywords, like searching documents on the WWW by search engines. We build an ontology to describe the concepts and relationships in the research domain and mine association rules by genetic network programming from the database where the attributes are concepts in ontology. By considering both the semantic similarity between the rules and the keywords, and the statistical information like support, confidence, chi-squared value, we could rank the rules by a new method named RuleRank, where genetic algorithm is applied to adjust the parameters and the optimal ranking model is built for the user. Experiments show that our approach is effective for the users to find what they want.